The aim of this study is to investigate the factors affecting the entrepreneurial intention (EI) of university students. In order to do so, EI, individual entrepreneurial orientation, self-efficacy, perceived educational support, perceived relational support, perceived structural support, knowledge sharing, and gender were used within the proposed model, and the constructed hypotheses were evaluated using SEM. The findings of a survey of 268 students show that self-efficacy is the strongest influencer of students’ EI. The findings also show the mediating influence of self-efficacy on the environmental components. Additionally, male students are more likely than female students to have EI.
The swift enhancement of technology has affected the business environment while higher education alone no longer plays a definitive role in the employment process. To meet the emerging requirements of employers, individuals, specifically students, need to develop more entrepreneurial tendencies. The aim of this study is to investigate the factors affecting the entrepreneurial intention (EI) of university students. In order to do so, eight constructs (EI, individual entrepreneurial orientation (IEO), self-efficacy, perceived educational support, perceived relational support, perceived structural support, knowledge sharing and gender) and their items taken from existing literature were used within the proposed model, and the constructed hypotheses were evaluated using structural equation modelling (SEM). In total, 268 surveys were returned by students of various universities. The findings of this study show that self-efficacy is the strongest influencer of students’ EI. The findings also show the mediating influence of self-efficacy on the environmental components. Additionally, male students are more likely than female students to have EI. Overall, this study will help establish the influencers of EI among university students.
Aims and objectives To gain insight into the perceived added value of a decision support App for district nurses and case managers intended to support a problem assessment and the provision of advices on possible solutions to facilitate ageing in place of people with dementia, and to investigate how they would implement the App in daily practice. Background District nurses and case managers play an important role in facilitating ageing in place of people with dementia (PwD). Detecting practical problems preventing PwD from living at home and advising on possible solutions is complex and challenging tasks for nurses and case managers. To support them with these tasks, a decision support App was developed. Methods A qualitative study using semi‐structured interviews was conducted. A photo‐elicitation method and an interview guide were used to structure the interviews. The data were analysed according to the principles of content analysis. Results In five interviews with seven district nurses and case managers, the added value was described in terms of five themes: (a) providing a broader/better overview of possible solutions; (b) providing a guideline/checklist for problem assessment and advice on solutions; (c) supporting an in‐depth problem assessment; (d) being a support tool for unexperienced case managers/district nurses; and (e) providing up‐to‐date information. The participants regarded the App as complementary to their current work procedure, which they would use in a flexible manner at different stages in the care continuum. Conclusions The participants valued both parts, the problem assessment and the overview of possible solutions. An important requisite for the usage would be that the content is continuously updated. Before implementation of the App can be recommended, an evaluation of its effectiveness regarding decision‐making should be conducted. Relevance to clinical practice This study underpins the need of nurses and case managers for decision support with regard to problem assessment and providing advices on possible solutions to facilitate ageing in place of PwD. There results also show the importance of listening to users experience and their perceived added value of decision support tools as this helps to explain the lack of statistically significant effects on quantitative outcome measure in contrast to a high willingness to use the App in a previous study.
